Overview

Dataset statistics

Number of variables12
Number of observations5262
Missing cells0
Missing cells (%)0.0%
Duplicate rows3
Duplicate rows (%)0.1%
Total size in memory493.4 KiB
Average record size in memory96.0 B

Variable types

Numeric11
Categorical1

Alerts

Dataset has 3 (0.1%) duplicate rowsDuplicates
Area is highly overall correlated with PriceHigh correlation
Bathrooms is highly overall correlated with Bedrooms and 1 other fieldsHigh correlation
Bedrooms is highly overall correlated with Bathrooms and 1 other fieldsHigh correlation
Frontages is highly overall correlated with Main roadHigh correlation
Main road is highly overall correlated with FrontagesHigh correlation
Price is highly overall correlated with Area and 3 other fieldsHigh correlation
Price_per_Square_Meter is highly overall correlated with PriceHigh correlation
Frontages has 4377 (83.2%) zerosZeros

Reproduction

Analysis started2024-06-21 08:42:56.990302
Analysis finished2024-06-21 08:43:13.185530
Duration16.2 seconds
Software versionydata-profiling v4.8.3
Download configurationconfig.json

Variables

Price
Real number (ℝ)

HIGH CORRELATION 

Distinct495
Distinct (%)9.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6084.7113
Minimum2
Maximum18420
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size41.2 KiB
2024-06-21T15:43:13.449038image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile1900
Q13900
median5450
Q37487.5
95-th percentile13000
Maximum18420
Range18418
Interquartile range (IQR)3587.5

Descriptive statistics

Standard deviation3292.9452
Coefficient of variation (CV)0.54118347
Kurtosis1.7176002
Mean6084.7113
Median Absolute Deviation (MAD)1650
Skewness1.2263261
Sum32017751
Variance10843488
MonotonicityNot monotonic
2024-06-21T15:43:13.619896image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4500 117
 
2.2%
5500 112
 
2.1%
6000 109
 
2.1%
6500 103
 
2.0%
4800 96
 
1.8%
5000 95
 
1.8%
5800 91
 
1.7%
4000 81
 
1.5%
4900 77
 
1.5%
5200 73
 
1.4%
Other values (485) 4308
81.9%
ValueCountFrequency (%)
2 2
< 0.1%
3 2
< 0.1%
3.3 1
< 0.1%
5.2 1
< 0.1%
5.9 1
< 0.1%
7.1 1
< 0.1%
7.58 1
< 0.1%
9.1 1
< 0.1%
10 1
< 0.1%
11.63 1
< 0.1%
ValueCountFrequency (%)
18420 1
 
< 0.1%
18400 2
 
< 0.1%
18000 17
0.3%
17900 6
 
0.1%
17600 2
 
< 0.1%
17550 1
 
< 0.1%
17500 10
0.2%
17380 1
 
< 0.1%
17000 20
0.4%
16900 1
 
< 0.1%

Area
Real number (ℝ)

HIGH CORRELATION 

Distinct228
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean59.844097
Minimum10
Maximum132
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size41.2 KiB
2024-06-21T15:43:13.787727image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile28
Q144
median56
Q374
95-th percentile104.985
Maximum132
Range122
Interquartile range (IQR)30

Descriptive statistics

Standard deviation23.095871
Coefficient of variation (CV)0.38593398
Kurtosis0.19366494
Mean59.844097
Median Absolute Deviation (MAD)15
Skewness0.68272648
Sum314899.64
Variance533.41924
MonotonicityNot monotonic
2024-06-21T15:43:13.953262image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60 327
 
6.2%
40 211
 
4.0%
80 208
 
4.0%
48 205
 
3.9%
52 173
 
3.3%
50 169
 
3.2%
56 142
 
2.7%
45 142
 
2.7%
100 119
 
2.3%
64 115
 
2.2%
Other values (218) 3451
65.6%
ValueCountFrequency (%)
10 1
 
< 0.1%
11 1
 
< 0.1%
12 6
 
0.1%
12.4 1
 
< 0.1%
13 1
 
< 0.1%
14 4
 
0.1%
14.6 1
 
< 0.1%
15 15
0.3%
16 7
0.1%
17 8
0.2%
ValueCountFrequency (%)
132 5
0.1%
131 3
 
0.1%
130 12
0.2%
129 4
 
0.1%
128 6
0.1%
126 6
0.1%
125 8
0.2%
124 5
0.1%
123 3
 
0.1%
122 3
 
0.1%

Bedrooms
Real number (ℝ)

HIGH CORRELATION 

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2495249
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size41.2 KiB
2024-06-21T15:43:14.083527image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median3
Q34
95-th percentile5
Maximum7
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.1344993
Coefficient of variation (CV)0.34912775
Kurtosis0.14953854
Mean3.2495249
Median Absolute Deviation (MAD)1
Skewness0.5730246
Sum17099
Variance1.2870887
MonotonicityNot monotonic
2024-06-21T15:43:14.200368image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
3 1627
30.9%
2 1465
27.8%
4 1417
26.9%
5 483
 
9.2%
6 133
 
2.5%
1 92
 
1.7%
7 45
 
0.9%
ValueCountFrequency (%)
1 92
 
1.7%
2 1465
27.8%
3 1627
30.9%
4 1417
26.9%
5 483
 
9.2%
6 133
 
2.5%
7 45
 
0.9%
ValueCountFrequency (%)
7 45
 
0.9%
6 133
 
2.5%
5 483
 
9.2%
4 1417
26.9%
3 1627
30.9%
2 1465
27.8%
1 92
 
1.7%

Bathrooms
Real number (ℝ)

HIGH CORRELATION 

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.1170658
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size41.2 KiB
2024-06-21T15:43:14.318284image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median3
Q34
95-th percentile5
Maximum7
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.2649327
Coefficient of variation (CV)0.40580878
Kurtosis0.072665928
Mean3.1170658
Median Absolute Deviation (MAD)1
Skewness0.70041427
Sum16402
Variance1.6000546
MonotonicityNot monotonic
2024-06-21T15:43:14.437262image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2 1768
33.6%
3 1476
28.1%
4 993
18.9%
5 546
 
10.4%
1 240
 
4.6%
6 177
 
3.4%
7 62
 
1.2%
ValueCountFrequency (%)
1 240
 
4.6%
2 1768
33.6%
3 1476
28.1%
4 993
18.9%
5 546
 
10.4%
6 177
 
3.4%
7 62
 
1.2%
ValueCountFrequency (%)
7 62
 
1.2%
6 177
 
3.4%
5 546
 
10.4%
4 993
18.9%
3 1476
28.1%
2 1768
33.6%
1 240
 
4.6%

Floors
Real number (ℝ)

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0286963
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size41.2 KiB
2024-06-21T15:43:14.557213image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median3
Q34
95-th percentile5
Maximum7
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.1760671
Coefficient of variation (CV)0.38830803
Kurtosis0.16367235
Mean3.0286963
Median Absolute Deviation (MAD)1
Skewness0.69558344
Sum15937
Variance1.3831338
MonotonicityNot monotonic
2024-06-21T15:43:14.679136image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2 1873
35.6%
3 1501
28.5%
4 1065
20.2%
5 438
 
8.3%
1 221
 
4.2%
6 131
 
2.5%
7 33
 
0.6%
ValueCountFrequency (%)
1 221
 
4.2%
2 1873
35.6%
3 1501
28.5%
4 1065
20.2%
5 438
 
8.3%
6 131
 
2.5%
7 33
 
0.6%
ValueCountFrequency (%)
7 33
 
0.6%
6 131
 
2.5%
5 438
 
8.3%
4 1065
20.2%
3 1501
28.5%
2 1873
35.6%
1 221
 
4.2%

Frontages
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct10
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.36944128
Minimum0
Maximum9
Zeros4377
Zeros (%)83.2%
Negative0
Negative (%)0.0%
Memory size41.2 KiB
2024-06-21T15:43:14.804383image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum9
Range9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.0641652
Coefficient of variation (CV)2.8804717
Kurtosis16.990542
Mean0.36944128
Median Absolute Deviation (MAD)0
Skewness3.8552737
Sum1944
Variance1.1324475
MonotonicityNot monotonic
2024-06-21T15:43:14.918459image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 4377
83.2%
1 445
 
8.5%
2 195
 
3.7%
4 110
 
2.1%
3 45
 
0.9%
5 44
 
0.8%
6 22
 
0.4%
7 14
 
0.3%
8 6
 
0.1%
9 4
 
0.1%
ValueCountFrequency (%)
0 4377
83.2%
1 445
 
8.5%
2 195
 
3.7%
3 45
 
0.9%
4 110
 
2.1%
5 44
 
0.8%
6 22
 
0.4%
7 14
 
0.3%
8 6
 
0.1%
9 4
 
0.1%
ValueCountFrequency (%)
9 4
 
0.1%
8 6
 
0.1%
7 14
 
0.3%
6 22
 
0.4%
5 44
 
0.8%
4 110
 
2.1%
3 45
 
0.9%
2 195
 
3.7%
1 445
 
8.5%
0 4377
83.2%

Main road
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size308.4 KiB
0.0
4218 
1.0
1044 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters15786
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 4218
80.2%
1.0 1044
 
19.8%

Length

2024-06-21T15:43:15.051078image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-21T15:43:15.170519image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 4218
80.2%
1.0 1044
 
19.8%

Most occurring characters

ValueCountFrequency (%)
0 9480
60.1%
. 5262
33.3%
1 1044
 
6.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 15786
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 9480
60.1%
. 5262
33.3%
1 1044
 
6.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 15786
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 9480
60.1%
. 5262
33.3%
1 1044
 
6.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 15786
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 9480
60.1%
. 5262
33.3%
1 1044
 
6.6%

Amenities
Real number (ℝ)

Distinct22
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.0779171
Minimum0
Maximum24
Zeros18
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size41.2 KiB
2024-06-21T15:43:15.295406image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median4
Q35
95-th percentile9
Maximum24
Range24
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.6056849
Coefficient of variation (CV)0.63897447
Kurtosis4.1559856
Mean4.0779171
Median Absolute Deviation (MAD)2
Skewness1.5158228
Sum21458
Variance6.789594
MonotonicityNot monotonic
2024-06-21T15:43:15.437138image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
3 1039
19.7%
4 861
16.4%
2 801
15.2%
1 708
13.5%
5 627
11.9%
6 433
8.2%
7 270
 
5.1%
8 185
 
3.5%
9 118
 
2.2%
10 69
 
1.3%
Other values (12) 151
 
2.9%
ValueCountFrequency (%)
0 18
 
0.3%
1 708
13.5%
2 801
15.2%
3 1039
19.7%
4 861
16.4%
5 627
11.9%
6 433
8.2%
7 270
 
5.1%
8 185
 
3.5%
9 118
 
2.2%
ValueCountFrequency (%)
24 1
 
< 0.1%
21 3
 
0.1%
20 1
 
< 0.1%
18 4
 
0.1%
17 5
 
0.1%
16 7
 
0.1%
15 3
 
0.1%
14 16
0.3%
13 13
 
0.2%
12 38
0.7%

Year_Fraction
Real number (ℝ)

Distinct385
Distinct (%)7.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2023.3542
Minimum2022.6822
Maximum2023.9425
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size41.2 KiB
2024-06-21T15:43:15.590816image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum2022.6822
5-th percentile2022.8384
Q12023.0055
median2023.3726
Q32023.6849
95-th percentile2023.8575
Maximum2023.9425
Range1.260274
Interquartile range (IQR)0.67945205

Descriptive statistics

Standard deviation0.35082234
Coefficient of variation (CV)0.00017338651
Kurtosis-1.4144393
Mean2023.3542
Median Absolute Deviation (MAD)0.32876712
Skewness-0.039184102
Sum10646890
Variance0.12307631
MonotonicityNot monotonic
2024-06-21T15:43:15.768957image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2022.821918 62
 
1.2%
2023.810959 54
 
1.0%
2022.835616 51
 
1.0%
2022.841096 41
 
0.8%
2022.917808 39
 
0.7%
2023.8 39
 
0.7%
2022.838356 37
 
0.7%
2023.813699 36
 
0.7%
2022.843836 35
 
0.7%
2023.761644 34
 
0.6%
Other values (375) 4834
91.9%
ValueCountFrequency (%)
2022.682192 1
 
< 0.1%
2022.684932 1
 
< 0.1%
2022.720548 4
 
0.1%
2022.764384 1
 
< 0.1%
2022.791781 1
 
< 0.1%
2022.819178 22
 
0.4%
2022.821918 62
1.2%
2022.824658 27
0.5%
2022.827397 17
 
0.3%
2022.830137 12
 
0.2%
ValueCountFrequency (%)
2023.942466 3
 
0.1%
2023.939726 9
0.2%
2023.936986 8
0.2%
2023.934247 4
 
0.1%
2023.931507 14
0.3%
2023.928767 9
0.2%
2023.926027 8
0.2%
2023.923288 3
 
0.1%
2023.920548 3
 
0.1%
2023.917808 5
 
0.1%

Price_per_Square_Meter
Real number (ℝ)

HIGH CORRELATION 

Distinct2424
Distinct (%)46.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean105.93177
Minimum0.03
Maximum260.87
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size41.2 KiB
2024-06-21T15:43:15.939547image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0.03
5-th percentile40.62
Q174.175
median100
Q3132.81
95-th percentile191.1925
Maximum260.87
Range260.84
Interquartile range (IQR)58.635

Descriptive statistics

Standard deviation45.758008
Coefficient of variation (CV)0.43195736
Kurtosis0.32081003
Mean105.93177
Median Absolute Deviation (MAD)28.85
Skewness0.53466589
Sum557412.98
Variance2093.7953
MonotonicityNot monotonic
2024-06-21T15:43:16.111438image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 112
 
2.1%
125 58
 
1.1%
75 46
 
0.9%
150 31
 
0.6%
120 31
 
0.6%
200 28
 
0.5%
83.33 28
 
0.5%
130 27
 
0.5%
80 24
 
0.5%
66.67 24
 
0.5%
Other values (2414) 4853
92.2%
ValueCountFrequency (%)
0.03 1
 
< 0.1%
0.04 2
< 0.1%
0.05 1
 
< 0.1%
0.06 1
 
< 0.1%
0.11 4
0.1%
0.13 1
 
< 0.1%
0.15 2
< 0.1%
0.18 2
< 0.1%
0.25 1
 
< 0.1%
0.31 1
 
< 0.1%
ValueCountFrequency (%)
260.87 1
 
< 0.1%
260 1
 
< 0.1%
257.14 2
< 0.1%
256.14 1
 
< 0.1%
255.56 1
 
< 0.1%
254.39 1
 
< 0.1%
254.07 1
 
< 0.1%
253.33 1
 
< 0.1%
252.94 3
0.1%
252.73 1
 
< 0.1%

location
Real number (ℝ)

Distinct20
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11829.133
Minimum11807.644
Maximum11843.541
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size41.2 KiB
2024-06-21T15:43:16.255465image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum11807.644
5-th percentile11822.853
Q111827.614
median11827.614
Q311830.81
95-th percentile11837.221
Maximum11843.541
Range35.896164
Interquartile range (IQR)3.1958999

Descriptive statistics

Standard deviation4.759488
Coefficient of variation (CV)0.00040235307
Kurtosis1.6077029
Mean11829.133
Median Absolute Deviation (MAD)3.1958999
Skewness0.68619402
Sum62244897
Variance22.652726
MonotonicityNot monotonic
2024-06-21T15:43:16.395161image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
11827.61398 1615
30.7%
11830.80988 933
17.7%
11825.43143 496
 
9.4%
11837.22065 426
 
8.1%
11822.85327 331
 
6.3%
11828.42098 319
 
6.1%
11834.35486 243
 
4.6%
11823.47834 173
 
3.3%
11843.54058 152
 
2.9%
11829.38655 118
 
2.2%
Other values (10) 456
 
8.7%
ValueCountFrequency (%)
11807.64442 5
 
0.1%
11816.69833 42
 
0.8%
11817.37756 29
 
0.6%
11819.90776 33
 
0.6%
11822.85327 331
 
6.3%
11823.47834 173
 
3.3%
11824.38464 68
 
1.3%
11825.43143 496
 
9.4%
11827.51656 61
 
1.2%
11827.61398 1615
30.7%
ValueCountFrequency (%)
11843.54058 152
 
2.9%
11837.22065 426
8.1%
11836.67102 51
 
1.0%
11834.47191 12
 
0.2%
11834.35486 243
 
4.6%
11831.73777 40
 
0.8%
11831.02232 115
 
2.2%
11830.80988 933
17.7%
11829.38655 118
 
2.2%
11828.42098 319
 
6.1%

District code
Real number (ℝ)

Distinct20
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.6708476
Minimum0
Maximum19
Zeros29
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size41.2 KiB
2024-06-21T15:43:16.532977image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q12
median6
Q315
95-th percentile19
Maximum19
Range19
Interquartile range (IQR)13

Descriptive statistics

Standard deviation6.3517544
Coefficient of variation (CV)0.73254135
Kurtosis-1.4111903
Mean8.6708476
Median Absolute Deviation (MAD)4
Skewness0.41517609
Sum45626
Variance40.344784
MonotonicityNot monotonic
2024-06-21T15:43:16.677468image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
2 1615
30.7%
6 933
17.7%
18 496
 
9.4%
17 426
 
8.1%
19 331
 
6.3%
5 319
 
6.1%
12 243
 
4.6%
13 173
 
3.3%
14 152
 
2.9%
8 118
 
2.2%
Other values (10) 456
 
8.7%
ValueCountFrequency (%)
0 29
 
0.6%
1 5
 
0.1%
2 1615
30.7%
3 42
 
0.8%
4 12
 
0.2%
5 319
 
6.1%
6 933
17.7%
7 51
 
1.0%
8 118
 
2.2%
9 40
 
0.8%
ValueCountFrequency (%)
19 331
6.3%
18 496
9.4%
17 426
8.1%
16 33
 
0.6%
15 115
 
2.2%
14 152
 
2.9%
13 173
 
3.3%
12 243
4.6%
11 68
 
1.3%
10 61
 
1.2%

Interactions

2024-06-21T15:43:11.417199image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-21T15:42:57.354717image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-21T15:42:58.936337image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-21T15:43:00.333303image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-21T15:43:01.660878image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-21T15:43:02.977471image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-21T15:43:04.435931image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-21T15:43:05.731756image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-21T15:43:07.126898image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-21T15:43:08.547967image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-21T15:43:10.011976image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-21T15:43:11.544975image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-21T15:42:57.479319image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-21T15:42:59.065564image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-21T15:43:00.461961image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-21T15:43:01.781465image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-21T15:43:03.108037image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-21T15:43:04.559198image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-21T15:43:05.864285image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-21T15:43:07.267050image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-21T15:43:08.817034image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-21T15:43:10.142143image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-21T15:43:11.660934image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-21T15:42:57.601637image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-21T15:42:59.181010image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
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2024-06-21T15:43:05.978564image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-21T15:43:07.390334image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-21T15:43:08.929477image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
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2024-06-21T15:43:11.785783image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-21T15:42:57.728646image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-21T15:42:59.298882image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-21T15:43:00.694800image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-21T15:43:02.012825image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-21T15:43:03.352158image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-21T15:43:04.789705image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
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2024-06-21T15:42:57.856345image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-21T15:42:59.495293image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-21T15:43:00.821565image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-21T15:43:02.127987image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-21T15:43:03.599933image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-21T15:43:04.911867image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-21T15:43:06.234458image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-21T15:43:07.661828image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-21T15:43:09.170909image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
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2024-06-21T15:43:12.047796image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-21T15:42:57.974460image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-21T15:42:59.615248image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-21T15:43:00.941418image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-21T15:43:02.244844image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
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2024-06-21T15:43:05.034351image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-21T15:43:06.361070image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-21T15:43:07.791387image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-21T15:43:09.288317image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-21T15:43:10.659072image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-21T15:43:12.164772image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-21T15:42:58.086463image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-21T15:42:59.730802image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-21T15:43:01.054632image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-21T15:43:02.353744image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-21T15:43:03.829051image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
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2024-06-21T15:43:06.476439image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-21T15:43:07.911350image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-21T15:43:09.399253image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-21T15:43:10.774718image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-21T15:43:12.282178image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-21T15:42:58.206210image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-21T15:42:59.846557image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
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2024-06-21T15:43:08.040617image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-21T15:43:09.519478image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-21T15:43:10.895554image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-21T15:43:12.414271image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-21T15:42:58.390955image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-21T15:42:59.965337image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-21T15:43:01.294803image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-21T15:43:02.597623image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-21T15:43:04.067684image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-21T15:43:05.374825image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-21T15:43:06.728101image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-21T15:43:08.167466image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-21T15:43:09.646426image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-21T15:43:11.029433image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-21T15:43:12.533900image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-21T15:42:58.515848image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-21T15:43:00.078488image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-21T15:43:01.409075image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-21T15:43:02.720390image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-21T15:43:04.183661image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-21T15:43:05.485231image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-21T15:43:06.843973image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-21T15:43:08.285802image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-21T15:43:09.760571image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-21T15:43:11.153624image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-21T15:43:12.666271image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-21T15:42:58.803794image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-21T15:43:00.201816image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-21T15:43:01.534653image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-21T15:43:02.849070image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-21T15:43:04.309500image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-21T15:43:05.608317image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-21T15:43:06.979456image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-21T15:43:08.417152image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-21T15:43:09.884961image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-21T15:43:11.284409image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Correlations

2024-06-21T15:43:16.794428image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
AmenitiesAreaBathroomsBedroomsDistrict codeFloorsFrontagesMain roadPricePrice_per_Square_MeterYear_Fractionlocation
Amenities1.0000.012-0.004-0.011-0.032-0.030-0.0590.0310.004-0.009-0.0270.020
Area0.0121.0000.3200.4090.0270.031-0.0120.0000.522-0.2490.0500.015
Bathrooms-0.0040.3201.0000.768-0.0260.027-0.0030.0280.6190.418-0.014-0.044
Bedrooms-0.0110.4090.7681.000-0.0040.018-0.0020.0000.5970.3120.042-0.036
District code-0.0320.027-0.026-0.0041.0000.0010.0150.106-0.027-0.0640.0280.016
Floors-0.0300.0310.0270.0180.0011.0000.0950.0980.0400.025-0.0000.029
Frontages-0.059-0.012-0.003-0.0020.0150.0951.0000.512-0.018-0.012-0.0140.063
Main road0.0310.0000.0280.0000.1060.0980.5121.000-0.022-0.0320.0020.086
Price0.0040.5220.6190.597-0.0270.040-0.018-0.0221.0000.632-0.030-0.016
Price_per_Square_Meter-0.009-0.2490.4180.312-0.0640.025-0.012-0.0320.6321.000-0.059-0.047
Year_Fraction-0.0270.050-0.0140.0420.028-0.000-0.0140.002-0.030-0.0591.0000.004
location0.0200.015-0.044-0.0360.0160.0290.0630.086-0.016-0.0470.0041.000

Missing values

2024-06-21T15:43:12.847707image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-06-21T15:43:13.080720image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

PriceAreaBedroomsBathroomsFloorsFrontagesMain roadAmenitiesYear_FractionPrice_per_Square_MeterlocationDistrict code
012800.075.05.06.02.00.00.03.02023.936986170.6711823.47833813
13400.0110.02.02.04.00.00.03.02023.93698630.9111825.43142618
28990.085.07.03.04.00.00.02.02023.936986105.7611834.35485812
37390.053.04.04.03.02.00.09.02023.936986139.4311837.22064917
416000.080.06.06.05.00.00.05.02023.936986200.0011827.6139832
54750.050.02.02.03.00.00.01.02023.93698695.0011834.4719104
65500.096.03.03.02.00.00.01.02023.93698657.2911822.85327219
75500.051.04.03.02.05.01.04.02023.939726107.8411828.4209785
89390.080.04.05.02.00.01.01.02023.942466117.3811827.51656010
94600.045.02.02.02.00.00.04.02023.939726102.2211827.6139832
PriceAreaBedroomsBathroomsFloorsFrontagesMain roadAmenitiesYear_FractionPrice_per_Square_MeterlocationDistrict code
52526900.060.04.03.04.00.00.01.02023.734247115.0011827.6139832
52535190.043.03.04.03.00.00.03.02023.734247120.7011825.43142618
52545200.065.04.05.03.00.00.02.02023.73424780.0011830.8098836
525514000.080.05.03.03.00.00.01.02023.734247175.0011827.6139832
52565500.080.03.03.06.00.00.01.02023.72602768.7511825.43142618
52574100.033.32.02.03.00.01.010.02023.726027123.1211843.54058014
52585200.040.03.04.02.01.01.01.02023.726027130.0011827.6139832
52597800.089.03.03.05.00.00.02.02023.72602787.6411825.43142618
52606680.080.04.04.01.02.01.07.02023.72602783.5011830.8098836
52617300.0112.04.04.03.00.00.04.02023.72602765.1811830.8098836

Duplicate rows

Most frequently occurring

PriceAreaBedroomsBathroomsFloorsFrontagesMain roadAmenitiesYear_FractionPrice_per_Square_MeterlocationDistrict code# duplicates
04250.065.03.02.02.00.00.02.02023.67397365.3811837.220649172
17000.090.04.03.02.00.00.03.02023.62739777.7811830.80988362
28500.0110.04.04.02.00.01.01.02022.96712377.2711830.80988362